# Building Machine Learning solutions with responsible AI
> Sketchnote by [Tomomi Imura](https://www.twitter.com/girlie_mac)
@ -13,9 +13,12 @@ Imagine what can happen when the data you are using to build these models lacks
In this lesson, you will:
- Raise your awareness of the importance of fairness in machine learning.
- Learn about fairness-related harms.
- Learn about unfairness assessment and mitigation.
- Raise your awareness of the importance of fairness in machine learning and fairness-related harms.
- Become familiar with the practice of exploring outliers and unusual scenarios to ensure reliability and safety
- Gain understanding on the need to empower everyone by designing inclusive systems
- Explore how vital it is to protect privacy and security of data and people
- See the importance of having a glass box approach to explain the behavior of AI models
- Be mindful of how accountability is essential to build trust in AI systems
## Prerequisite
@ -34,35 +37,36 @@ AI systems should treat everyone fairly and avoid affecting similar groups of pe
**“Unfairness”** encompasses negative impacts, or “harms”, for a group of people, such as those defined in terms of race, gender, age, or disability status. The main fairness-related harms can be classified as:
- **Allocation**, if a gender or ethnicity for example is favored over another.
- **Quality of service**. If you train the data for one specific scenario but reality is much more complex, it leads to a poor performing service.
- **Stereotyping**. Associating a given group with pre-assigned attributes.
- **Denigration**. To unfairly criticize and label something or someone.
- **Quality of service**. If you train the data for one specific scenario but reality is much more complex, it leads to a poor performing service. For instance, a hand soap dispenser that could not seem to be able to sense people with dark skin. [Reference](https://gizmodo.com/why-cant-this-soap-dispenser-identify-dark-skin-1797931773)
- **Denigration**. To unfairly criticize and label something or someone. For example, an image labeling technology infamously mislabeled images of dark-skinned people as gorillas.
- **Over- or under- representation**. The idea is that a certain group is not seen in a certain profession, and any service or function that keeps promoting that is contributing to harm.
- **Stereotyping**. Associating a given group with pre-assigned attributes. For example, a language translation system betweem English and Turkish may have inaccuraces due to words with stereotypical associations to gender.
When designing and testing AI systems, we need to ensure that AI is fair and not programmed to make biased or discriminatory decisions, which human beings are also prohibited from making. Guaranteeing fairness in AI and machine learning remains a complex sociotechnical challenge.
> translation to Turkish
### Reliability and safety
> translation back to English
To build trust, AI systems need to be reliable, safe, and consistent under normal and unexpected conditions. It is important to know how AI systems will behavior in a variety of situations, especially when they are outliers. When building AI solutions, there needs to be a substantial amount of focus on how to handle a wide variety of circumstances that the AI solutions would encounter.
When designing and testing AI systems, we need to ensure that AI is fair and not programmed to make biased or discriminatory decisions, which human beings are also prohibited from making. Guaranteeing fairness in AI and machine learning remains a complex sociotechnical challenge.
For example, a self-driving car needs to put people's safety as a top priority. As a result, the AI powering the car need to consider all the possible scenarios that the car could come across such as night, thunderstorms or blizzards, kids running across the street, pets, road constructions etc. How well an AI system can handle a wild range of conditions reliably and safely reflects the level of anticipation the data scientist or AI developer considered during the design or testing of the system.
### Reliability and safety
<!-- [](https://youtu.be/dnC8-uUZXSc "Microsoft's Approach to Responsible AI")
To build trust, AI systems need to be reliable, safe, and consistent under normal and unexpected conditions. It is important to know how AI systems will behavior in a variety of situations, especially when they are outliers. When building AI solutions, there needs to be a substantial amount of focus on how to handle a wide variety of circumstances that the AI solutions would encounter. For example, a self-driving car needs to put people's safety as a top priority. As a result, the AI powering the car need to consider all the possible scenarios that the car could come across such as night, thunderstorms or blizzards, kids running across the street, pets, road constructions etc. How well an AI system can handle a wild range of conditions reliably and safely reflects the level of anticipation the data scientist or AI developer considered during the design or testing of the system.
> 🎥 Click the image above for a video: Ensure reliability and safety in AI -->
> [🎥 Click the here for a video: ](https://www.microsoft.com/videoplayer/embed/RE4vvIl)
### Inclusiveness
AI systems should be designed to engage and empower everyone. When designing and implementing AI systems data scientists and AI developers identify and address potential barriers in the system that could unintentionally exclude people. For example, there are 1 billion people with disabilities around the world. With the advancement of AI, they can access a wide range of information and opportunities more easily in their daily lives. By addressing the barriers, it creates opportunities to innovate and develop AI products with better experiences that benefit everyone.
> Inclusive systems for accessibility
> [🎥 Click the here for a video: inclusiveness in AI](https://www.microsoft.com/videoplayer/embed/RE4vl9v)
### Security and privacy
AI systems should be safe and respect people’s privacy. People have less trust in systems that put their privacy, information, or lives at risk. When training machine learning models, we rely on data to produce the best results. In doing so, the origin of the data and integrity must be considered. For example, was the data user submitted or publicly available?
AI systems should be safe and respect people’s privacy. People have less trust in systems that put their privacy, information, or lives at risk. When training machine learning models, we rely on data to produce the best results. In doing so, the origin of the data and integrity must be considered. For example, was the data user submitted or publicly available? Next, while working with the data, it is crucial to develop AI systems that can protect confidential information and resist attacks. As AI becomes more prevalent, protecting privacy and securing important personal and business information is becoming more critical and complex. Privacy and data security issues require especially close attention for AI because access to data is essential for AI systems to make accurate and informed predictions and decisions about people.
Next, while working with the data, it is crucial to develop AI systems that can protect confidential information and resist attacks. As AI becomes more prevalent, protecting privacy and securing important personal and business information is becoming more critical and complex. Privacy and data security issues require especially close attention for AI because access to data is essential for AI systems to make accurate and informed predictions and decisions about people.
> [🎥 Click the here for a video: security in AI](https://www.microsoft.com/videoplayer/embed/RE4voJF)
- As an industry we have made significant advancements in Privacy & security, fueled significantly by regulations like the GDPR (General Data Protection Regulation).
- Yet with AI systems we must acknowledge the tension between the need for more personal data to make systems more personal and effective – and privacy.
@ -72,9 +76,9 @@ Next, while working with the data, it is crucial to develop AI systems that can
### Transparency
AI systems should be understandable. A crucial part of transparency is explaining the behavior of AI systems and their components. Improving the understanding of AI systems requires that stakeholders comprehend how and why they function so that they can identify potential performance issues, safety and privacy concerns, biases, exclusionary practices, or unintended outcomes. We also believe that those who use AI systems should be honest and forthcoming about when, why, and how they choose to deploy them. As well as the limitations of the systems they use.
AI systems should be understandable. A crucial part of transparency is explaining the behavior of AI systems and their components. Improving the understanding of AI systems requires that stakeholders comprehend how and why they function so that they can identify potential performance issues, safety and privacy concerns, biases, exclusionary practices, or unintended outcomes. We also believe that those who use AI systems should be honest and forthcoming about when, why, and how they choose to deploy them. As well as the limitations of the systems they use. For example, if a bank uses an AI system to support its consumer lending decisions, it is important to examine the outcomes and understand which data influences the system’s recommendations. Governments are starting to regulate AI across industries, so data scientists and organizations must explain if an AI system meets regulatory requirements, especially when there is an undesirable outcome.
For example, if a bank uses an AI system to support its consumer lending decisions, it is important to examine the outcomes and understand which data influences the system’s recommendations. Governments are starting to regulate AI across industries, so data scientists and organizations must explain if an AI system meets regulatory requirements, especially when there is an undesirable outcome.
> [🎥 Click the here for a video: transparency in AI](https://www.microsoft.com/videoplayer/embed/RE4voJF)
- Because AI systems are so complex, it is hard to understand how they work and interpret the results.
- This lack of understanding affects the way these systems are managed, operationalized, and documented.
@ -88,141 +92,39 @@ The people who design and deploy AI systems must be accountable for how their sy
> 🎥 Click the image above for a video: Warnings of Mass Surveillance Through Facial Recognition
One of the biggest questions for our generation, as the first generation that is bringing AI to society, is how to ensure that computers will remain accountable to people and how to ensure that the people that design computers remain accountable to everyone else.
Let us look at the examples.
#### Allocation
Consider a hypothetical system for screening loan applications. The system tends to pick white men as better candidates over other groups. As a result, loans are withheld from certain applicants.
Another example would be an experimental hiring tool developed by a large corporation to screen candidates. The tool systemically discriminated against one gender by using the models were trained to prefer words associated with another. It resulted in penalizing candidates whose resumes contain words such as “women’s rugby team”.
✅ Do a little research to find a real-world example of something like this.
#### Quality of Service
Researchers found that several commercial gender classifiers had higher error rates around images of women with darker skin tones as opposed to images of men with lighter skin tones. [Reference](https://www.media.mit.edu/publications/gender-shades-intersectional-accuracy-disparities-in-commercial-gender-classification/)
Another infamous example is a hand soap dispenser that could not seem to be able to sense people with dark skin. [Reference](https://gizmodo.com/why-cant-this-soap-dispenser-identify-dark-skin-1797931773)
Ultimately one of the biggest questions for our generation, as the first generation that is bringing AI to society, is how to ensure that computers will remain accountable to people and how to ensure that the people that design computers remain accountable to everyone else.
#### Stereotyping
A stereotypical gender view was found in machine translation. When translating “he is a nurse and she is a doctor” into Turkish, problems were encountered. Turkish is a genderless language which has one pronoun, “o” to convey a singular third person, but translating the sentence back from Turkish to English yields the stereotypical and incorrect as “she is a nurse, and he is a doctor.”
## Impact assessment
> translation to Turkish
> translation back to English
#### Denigration
An image labeling technology infamously mislabeled images of dark-skinned people as gorillas. Mislabeling is harmful not just because the system made a mistake because it specifically applied a label that has a long history of being purposefully used to denigrate Black people.
[](https://www.youtube.com/watch?v=QxuyfWoVV98 "AI, Ain't I a Woman?")
> 🎥 Click the image above for a video: AI, Ain't I a Woman - a performance showing the harm caused by racist denigration by AI
Before training a machine learning model, it is important to conduct an impact assessmet to understand the purpose of the AI system; what the intended use is; where it will be deployed; and who will be interacting with the system. These are helpful for reviewer(s) or testers evaluating the system to know what factors to take into consideration when identifying potential risks and expected consequences.
#### Over-representation or under-representation
Skewed image search results can be a good example of this harm. When searching images of professions with an equal or higher percentage of men than women, such as engineering, or CEO, watch for results that are more heavily skewed towards a given gender.
The following are areas of focus when conducting an impact assessment:
> This search on Bing for ‘CEO’ produces inclusive results
* **Adverse impact on individuals**. Being aware of any restriction or requirements, unsupported use or any known limitations hindering the system's performance is vital to ensure that the system is not used in a way that could cause harm to individuals.
* **Data requirements**. Gaining an understanding of how and where the system will use data enables reviewers to explore any data requirements you would need to be mindful of (e.g., GDPR or HIPPA data regulations). In addition, examine whether the source or quantity of data is substantial for training.
* **Summary of impact**. Gather a list of potential harms that could arise from using the system. Throughout the ML lifecycle, review if the issues identified are mitigated or addressed.
* **Applicable goals** for each of the six core principles. Assess if the goals from each of the principles are met and if there are any gaps.
These five main types of harm are not mutually exclusive, and a single system can exhibit more than one type of harm. In addition, each case varies in its severity. For instance, unfairly labeling someone as a criminal is a much more severe harm than mislabeling an image. It is important, however, to remember that even relatively non-severe harms can make people feel alienated or singled out and the cumulative impact can be extremely oppressive.
✅ **Discussion**: Revisit some of the examples and see if they show different harms.
## Debugging with responsible AI
| | Allocation | Quality of service | Stereotyping | Denigration | Over- or under- representation |
There are many reasons why a given system behaves unfairly. Social biases, for example, might be reflected in the datasets used to train them. For example, hiring unfairness might have been exacerbated by over reliance on historical data. By using the patterns in resumes submitted to the company over a 10-year period, the model determined that men were more qualified because many resumes came from men, a reflection of past male dominance across the tech industry.
Inadequate data about a certain group of people can be the reason for unfairness. For example, image classifiers have a higher rate of error for images of dark-skinned people because darker skin tones were underrepresented in the data.
Wrong assumptions made during development cause unfairness too. For example, a facial analysis system intended to predict who is going to commit a crime based on images of people’s faces can lead to damaging assumptions. This could lead to substantial harm for people who are misclassified.
## Understand your models and build in fairness
Although many aspects of fairness are not captured in quantitative fairness metrics, and it is not possible to fully remove bias from a system to guarantee fairness, you are still responsible to detect and to mitigate fairness issues as much as possible.
Similar to debugging a software application, debugging an AI system is a necessary process of identifying and resolving issues in the system. There are many factors that would affect a model not performing as expected or responsibly. Most traditional model performance metrics are quantitative aggregates of a model's performance, which are not sufficient to analyze how a model violates the responsible AI principles. Furthermore, a machine learning model is a black box that makes it difficult to understand what drives its outcome or provide explanation when it makes a mistake. Later in this course, we will learn how to use the Responsible AI dashboard to help debug AI systems. The dashboard provides a holistic tool for data scientists and AI developers to perform:
When you are working with machine learning models, it is important to understand your models by means of assuring their interpretability and by assessing and mitigating unfairness.
* **Error analysis**. To identify the error distribution of the model that can affect the system's fairness or reliability.
* **Model overview**. To discover where there are disparities in the model's performance across data cohorts.
* **Data analysis**. To understand the data distribution and identify any potential bias in the data that could lead to fairness, inclusiveness, and reliability issues.
* **Model interpretability**. To understand what affects or influences the model's predictions. This helps in explaining the model's behavior, which is important for transparency and accountability.
Let’s use the loan selection example to isolate the case to figure out each factor's level of impact on the prediction.
## Assessment methods
1. **Identify harms (and benefits)**. The first step is to identify harms and benefits. Think about how actions and decisions can affect both potential customers and a business itself.
1. **Identify the affected groups**. Once you understand what kind of harms or benefits that can occur, identify the groups that may be affected. Are these groups defined by gender, ethnicity, or social group?
1. **Define fairness metrics**. Finally, define a metric so you have something to measure against in your work to improve the situation.
### Identify harms (and benefits)
What are the harms and benefits associated with lending? Think about false negatives and false positive scenarios:
**False negatives** (reject, but Y=1) - in this case, an applicant who will be capable of repaying a loan is rejected. This is an adverse event because the resources of the loans are withheld from qualified applicants.
**False positives** (accept, but Y=0) - in this case, the applicant does get a loan but eventually defaults. As a result, the applicant's case will be sent to a debt collection agency which can affect their future loan applications.
### Identify affected groups
The next step is to determine which groups are likely to be affected. For example, in case of a credit card application, a model might determine that women should receive much lower credit limits compared with their spouses who share household assets. An entire demographic, defined by gender, is thereby affected.
### Define fairness metrics
You have identified harms and an affected group, in this case, delineated by gender. Now, use the quantified factors to disaggregate their metrics. For example, using the data below, you can see that women have the largest false positive rate and men have the smallest, and that the opposite is true for false negatives.
✅ In a future lesson on Clustering, you will see how to build this 'confusion matrix' in code
This table tells us several things. First, we note that there are comparatively few non-binary people in the data. The data is skewed, so you need to be careful how you interpret these numbers.
In this case, we have 3 groups and 2 metrics. When we are thinking about how our system affects the group of customers with their loan applicants, this may be sufficient, but when you want to define larger number of groups, you may want to distill this to smaller sets of summaries. To do that, you can add more metrics, such as the largest difference or smallest ratio of each false negative and false positive.
✅ Stop and Think: What other groups are likely to be affected for loan application?
## Mitigating unfairness
To mitigate unfairness, explore the model to generate various mitigated models and compare the tradeoffs it makes between accuracy and fairness to select the most fair model.
This introductory lesson does not dive deeply into the details of algorithmic unfairness mitigation, such as post-processing and reductions approach, but here is a tool that you may want to try.
### Fairlearn
[Fairlearn](https://fairlearn.github.io/) is an open-source Python package that allows you to assess your systems' fairness and mitigate unfairness.
The tool helps you to assesses how a model's predictions affect different groups, enabling you to compare multiple models by using fairness and performance metrics, and supplying a set of algorithms to mitigate unfairness in binary classification and regression.
- Learn how to use the different components by checking out the Fairlearn's [GitHub](https://github.com/fairlearn/fairlearn/)
- Explore the [user guide](https://fairlearn.github.io/main/user_guide/index.html), [examples](https://fairlearn.github.io/main/auto_examples/index.html)
- Try some [sample notebooks](https://github.com/fairlearn/fairlearn/tree/master/notebooks).
- Learn [how to enable fairness assessments](https://docs.microsoft.com/azure/machine-learning/how-to-machine-learning-fairness-aml?WT.mc_id=academic-77952-leestott) of machine learning models in Azure Machine Learning.
- Check out these [sample notebooks](https://github.com/Azure/MachineLearningNotebooks/tree/master/contrib/fairness) for more fairness assessment scenarios in Azure Machine Learning.
---
## 🚀 Challenge
To prevent biases from being introduced in the first place, we should:
To prevent harms from being introduced in the first place, we should:
- have a diversity of backgrounds and perspectives among the people working on systems
- invest in datasets that reflect the diversity of our society
- develop better methods for detecting and correcting bias when it occurs
- develop better methods throughout the machine learning lifecycle for detecting and correcting responible AI when it occurs
Think about real-life scenarios where unfairness is evident in model-building and usage. What else should we consider?
Think about real-life scenarios where a model's untrustworthiness is evident in model-building and usage. What else should we consider?
@ -231,10 +133,11 @@ In this lesson, you have learned some basics of the concepts of fairness and unf
Watch this workshop to dive deeper into the topics:
- Fairness-related harms in AI systems: Examples, assessment, and mitigation by Hanna Wallach and Miro Dudik
- In pursuit of responsible AI: Bringing principles to practice by Besmira Nushi, Mehrnoosh Sameki and Amit Sharma
[](https://www.youtube.com/watch?v=tGgJCrA-MZU "RAI Toolbox: An open-source framework for building responsible AI")
[](https://www.youtube.com/watch?v=1RptHwfkx_k "Fairness-related harms in AI systems: Examples, assessment, and mitigation")
> 🎥 Click the image above for a video: Fairness-related harms in AI systems: Examples, assessment, and mitigation by Hanna Wallach and Miro Dudik
> 🎥 Click the image above for a video: RAI Toolbox: An open-source framework for building responsible AI by Besmira Nushi, Mehrnoosh Sameki, and Amit Sharma
Also, read:
@ -242,9 +145,9 @@ Also, read:
- Microsoft’s FATE research group: [FATE: Fairness, Accountability, Transparency, and Ethics in AI - Microsoft Research](https://www.microsoft.com/research/theme/fate/)
Explore the Fairlearn toolkit:
RAI Toolbox:
- [Fairlearn](https://fairlearn.org/)
- [Responsible AI Toolbox GitHub repository](https://github.com/microsoft/responsible-ai-toolbox)
Read about Azure Machine Learning's tools to ensure fairness:
@ -252,4 +155,4 @@ Read about Azure Machine Learning's tools to ensure fairness:
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